This machine-learning system can simulate how a listener would hear a sound from any point in a room.

Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time.

Neuroscience professor and Science Hub investigator Ted Adelson explains how simulating the sense of touch with a camera can make robots smarter.

This robotic system uses radio frequency signals, computer vision, and complex reasoning to efficiently find items hidden under a pile.

MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.

A new technique in computer vision may enhance our three-dimensional understanding of two-dimensional images.

The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.

A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.

MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.

“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.